{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "In this notebook, we'll build a simple math agent using the Mistral Agents API, leveraging its built-in code execution tool to solve mathematical problems."
      ],
      "metadata": {
        "id": "JdsELI-j6lrm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Setting up dependencies\n"
      ],
      "metadata": {
        "id": "Ky4Juhb18QzO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.1 Installing the Mistral library"
      ],
      "metadata": {
        "id": "PR9WEUJU8arx"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "5Newocj7H45I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d8042d1-184e-46e9-9201-2565f0dd312a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting mistralai\n",
            "  Downloading mistralai-1.8.1-py3-none-any.whl.metadata (33 kB)\n",
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            "\u001b[?25hDownloading eval_type_backport-0.2.2-py3-none-any.whl (5.8 kB)\n",
            "Installing collected packages: eval-type-backport, mistralai\n",
            "Successfully installed eval-type-backport-0.2.2 mistralai-1.8.1\n"
          ]
        }
      ],
      "source": [
        "!pip install mistralai"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.2 Loading the Mistral API Key\n",
        "You can get an API key from https://console.mistral.ai/api-keys"
      ],
      "metadata": {
        "id": "PahfSqIn8fpX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from getpass import getpass\n",
        "apiKey = getpass('Enter Mistral API Key: ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1818ffjdwN8O",
        "outputId": "cbdd589a-3964-416e-8a70-ebe08e4c05c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter Mistral API Key: ··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Creating the Mistral client and Agent"
      ],
      "metadata": {
        "id": "qP0zbYdY8vte"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following code creates a custom math agent using the Mistral Agents API. The agent, named **Math Helper**, is configured to solve mathematical problems, evaluate expressions, and explain concepts. It uses the *mistral-medium-2505* model along with Mistral's built-in *code_interpreter* tool, allowing it to run Python code when needed. The agent is initialized with clear instructions and tuned with specific completion parameters to ensure accurate and focused responses."
      ],
      "metadata": {
        "id": "2llw6g1582ZF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mistralai import Mistral\n",
        "client = Mistral(apiKey)"
      ],
      "metadata": {
        "id": "g-aWaWUmwXOg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "math_agent = client.beta.agents.create(\n",
        "    model=\"mistral-medium-2505\",\n",
        "    description=\"An agent that solves math problems and evaluates expressions.\",\n",
        "    name=\"Math Helper\",\n",
        "    instructions=\"You are a helpful math assistant. You can explain concepts, solve equations, and evaluate math expressions using the code interpreter.\",\n",
        "    tools=[{\"type\": \"code_interpreter\"}],\n",
        "    completion_args={\n",
        "        \"temperature\": 0.2,\n",
        "        \"top_p\": 0.9\n",
        "    }\n",
        ")"
      ],
      "metadata": {
        "id": "AxKyf9A-wmF3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Running the Agent"
      ],
      "metadata": {
        "id": "8zdSvwTF9U5Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.1 Initializing the conversation\n",
        "\n",
        "The following code initiates a new conversation with the math_agent, asking it to solve the quadratic equation 2x² + 3x - 2 = 0. The start() method sends the input query to the agent, which uses the specified model and tools (like the code interpreter) to generate a response. The result, including the assistant’s explanation and code execution, is stored in the response variable."
      ],
      "metadata": {
        "id": "aPNRkhwr9lcf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "response = client.beta.conversations.start(\n",
        "    agent_id=math_agent.id, inputs=\"Solve the quadratic equation 2x² + 3x - 2 = 0\", #store=False\n",
        ")\n",
        "\n",
        "response"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iuSNgA7Mwpkv",
        "outputId": "652ea344-0ef5-4dce-e28f-4b2526ea1f79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ConversationResponse(conversation_id='conv_0683c1b8dfff72d9800003f28f1ea202', outputs=[MessageOutputEntry(content=\"To solve the quadratic equation \\\\(2x^2 + 3x - 2 = 0\\\\), we can use the quadratic formula:\\n\\n\\\\[ x = \\\\frac{-b \\\\pm \\\\sqrt{b^2 - 4ac}}{2a} \\\\]\\n\\nwhere \\\\(a = 2\\\\), \\\\(b = 3\\\\), and \\\\(c = -2\\\\).\\n\\nLet's calculate the solutions step-by-step.\", object='entry', type='message.output', created_at=datetime.datetime(2025, 6, 1, 9, 21, 18, 172555, tzinfo=TzInfo(UTC)), completed_at=datetime.datetime(2025, 6, 1, 9, 21, 18, 787860, tzinfo=TzInfo(UTC)), id='msg_0683c1b8e2c279e280004acabf39b047', agent_id='ag_0683c1ae5180761780008f1cce902c71', model='mistral-medium-2505', role='assistant'), ToolExecutionEntry(name='code_interpreter', object='entry', type='tool.execution', created_at=datetime.datetime(2025, 6, 1, 9, 21, 18, 897177, tzinfo=TzInfo(UTC)), completed_at=datetime.datetime(2025, 6, 1, 9, 21, 21, 254994, tzinfo=TzInfo(UTC)), id='tool_exec_0683c1b8ee5a7ad78000bc59004dab7a', info={'code': '# Coefficients from the quadratic equation: 2x² + 3x - 2 = 0\\nimport math\\n\\n# Define the coefficients\\na = 2\\nb = 3\\nc = -2\\n\\n# Calculate the discriminant\\nD = b**2 - 4*a*c\\n\\n# Calculate the two solutions\\nx1 = (-b + math.sqrt(D)) / (2*a)\\nx2 = (-b - math.sqrt(D)) / (2*a)\\n\\nx1, x2', 'code_output': '(0.5, -2.0)\\n'}), MessageOutputEntry(content='The solutions to the quadratic equation \\\\(2x^2 + 3x - 2 = 0\\\\) are \\\\(x = 0.5\\\\) and \\\\(x = -2.0\\\\).', object='entry', type='message.output', created_at=datetime.datetime(2025, 6, 1, 9, 21, 21, 325654, tzinfo=TzInfo(UTC)), completed_at=datetime.datetime(2025, 6, 1, 9, 21, 21, 702424, tzinfo=TzInfo(UTC)), id='msg_0683c1b915357b6e8000c1a037df7cb6', agent_id='ag_0683c1ae5180761780008f1cce902c71', model='mistral-medium-2505', role='assistant')], usage=ConversationUsageInfo(prompt_tokens=113, completion_tokens=241, total_tokens=373, connector_tokens=19, connectors={'code_interpreter': 1}), object='conversation.response')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.2 Final Output"
      ],
      "metadata": {
        "id": "O9D82a48908O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "response.outputs[2].content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "DNnE1OkOw85S",
        "outputId": "72cd6cf6-f0a7-462f-85f0-ccbc8909c650"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The solutions to the quadratic equation \\\\(2x^2 + 3x - 2 = 0\\\\) are \\\\(x = 0.5\\\\) and \\\\(x = -2.0\\\\).'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.3 Executed Code"
      ],
      "metadata": {
        "id": "JL4uHR-B93s7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(response.outputs[1].info['code'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tFXNNM7axMsD",
        "outputId": "0620978e-8f81-49ea-9438-5b8c2f08ad58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# Coefficients from the quadratic equation: 2x² + 3x - 2 = 0\n",
            "import math\n",
            "\n",
            "# Define the coefficients\n",
            "a = 2\n",
            "b = 3\n",
            "c = -2\n",
            "\n",
            "# Calculate the discriminant\n",
            "D = b**2 - 4*a*c\n",
            "\n",
            "# Calculate the two solutions\n",
            "x1 = (-b + math.sqrt(D)) / (2*a)\n",
            "x2 = (-b - math.sqrt(D)) / (2*a)\n",
            "\n",
            "x1, x2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.4 Continuing the conversation\n",
        "This ensures that the agent retains the context and builds upon the previous interactions, allowing for a more natural and coherent dialogue."
      ],
      "metadata": {
        "id": "LqhlTjak-UOB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "response = client.beta.conversations.append(\n",
        "    conversation_id=response.conversation_id, inputs=\"Plot the function f(x) = 2x² + 3x - 2\"\n",
        ")"
      ],
      "metadata": {
        "id": "UIlWgyH0xS_S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response.outputs[2].content[1].text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "aO0_X3w_1RE0",
        "outputId": "34ae816f-48e0-44b2-e338-b0cfbfc34133"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Here is the plot of the function \\\\( f(x) = 2x^2 + 3x - 2 \\\\). It is a parabola opening upwards, characteristic of quadratic functions with a positive coefficient for \\\\(x^2\\\\). The roots of the equation, where the curve intersects the x-axis, are at \\\\(x = 0.5\\\\) and \\\\(x = -2.0\\\\), as previously calculated.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(response.outputs[1].info['code'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6W0UeIO0-tTN",
        "outputId": "b09e1607-83f0-424f-a868-16939cf36bd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "import numpy as np\n",
            "import matplotlib.pyplot as plt\n",
            "\n",
            "# Define the function\n",
            "def f(x):\n",
            "    return 2*x**2 + 3*x - 2\n",
            "\n",
            "# Generate x values\n",
            "x_values = np.linspace(-5, 5, 400)\n",
            "# Calculate corresponding y values\n",
            "y_values = f(x_values)\n",
            "\n",
            "# Plot the function\n",
            "plt.figure(figsize=(8, 6))\n",
            "plt.plot(x_values, y_values, label='$f(x) = 2x^2 + 3x - 2$')\n",
            "plt.title('Plot of the function $f(x) = 2x^2 + 3x - 2$')\n",
            "plt.xlabel('x')\n",
            "plt.ylabel('f(x)')\n",
            "plt.axhline(0, color='black', linewidth=0.8)  # x-axis\n",
            "plt.axvline(0, color='black', linewidth=0.8)  # y-axis\n",
            "plt.grid(True)\n",
            "plt.legend()\n",
            "plt.show()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3.5 Downloading the generated image and plotting it"
      ],
      "metadata": {
        "id": "kvmUc8uf-m0u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "file_id = response.outputs[2].content[0].file_id\n",
        "file_bytes = client.files.download(file_id=file_id).read()"
      ],
      "metadata": {
        "id": "ZCahrQxa5MmQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(f\"image_generated.png\", \"wb\") as file:\n",
        "    file.write(file_bytes)"
      ],
      "metadata": {
        "id": "kmo3re1X6L7Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This will download the generated image as ***image_generated.png*** in the current directory. We can display the the same using the following code"
      ],
      "metadata": {
        "id": "mPTqFijV-8ha"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Image, display\n",
        "image_path = \"image_generated.png\"\n",
        "\n",
        "display(Image(filename=image_path))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "id": "zaWmtreN6XCK",
        "outputId": "3996e59a-4e38-46cc-99fb-abc367be1c99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}